18850
26700
sklearn.preprocessing._encoders.OneHotEncoder
sklearn.OneHotEncoder
sklearn.preprocessing._encoders.OneHotEncoder
27
openml==0.12.2,sklearn==0.22.2.post1
Encode categorical features as a one-hot numeric array.
The input to this transformer should be an array-like of integers or
strings, denoting the values taken on by categorical (discrete) features.
The features are encoded using a one-hot (aka 'one-of-K' or 'dummy')
encoding scheme. This creates a binary column for each category and
returns a sparse matrix or dense array (depending on the ``sparse``
parameter)
By default, the encoder derives the categories based on the unique values
in each feature. Alternatively, you can also specify the `categories`
manually.
This encoding is needed for feeding categorical data to many scikit-learn
estimators, notably linear models and SVMs with the standard kernels.
Note: a one-hot encoding of y labels should use a LabelBinarizer
instead.
2021-07-06T13:15:55
English
sklearn==0.22.2.post1
numpy>=1.11.0
scipy>=0.17.0
joblib>=0.11
categories
'auto' or a list of array
"auto"
Categories (unique values) per feature:
- 'auto' : Determine categories automatically from the training data
- list : ``categories[i]`` holds the categories expected in the ith
column. The passed categories should not mix strings and numeric
values within a single feature, and should be sorted in case of
numeric values
The used categories can be found in the ``categories_`` attribute
drop
'first' or a array
null
Specifies a methodology to use to drop one of the categories per
feature. This is useful in situations where perfectly collinear
features cause problems, such as when feeding the resulting data
into a neural network or an unregularized regression
dtype
number type
{"oml-python:serialized_object": "type", "value": "np.float64"}
Desired dtype of output
handle_unknown : {'error', 'ignore'}, default='error'
Whether to raise an error or ignore if an unknown categorical feature
is present during transform (default is to raise). When this parameter
is set to 'ignore' and an unknown category is encountered during
transform, the resulting one-hot encoded columns for this feature
will be all zeros. In the inverse transform, an unknown category
will be denoted as None.
handle_unknown
"ignore"
sparse
bool
false
Will return sparse matrix if set True else will return an array
openml-python
python
scikit-learn
sklearn
sklearn_0.22.2.post1